{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实现 ARMA模型预测（专业课）\n",
    "# 使用discover数据集或自己收集的本专业相关数据、构建相应的AR、MA或ARMA模型，预测出之后至少10个步长的数据值，并将预测值与真实值进行对比。\n",
    "# 实验应至少包括：数据预处理、数据可视化、模型的识别与定阶、模型预测、预测值与真实值对比图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20100104</td>\n",
       "      <td>24.52</td>\n",
       "      <td>24.58</td>\n",
       "      <td>23.68</td>\n",
       "      <td>23.71</td>\n",
       "      <td>24.37</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-2.7100</td>\n",
       "      <td>241922.76</td>\n",
       "      <td>5.802495e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20100105</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.90</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.71</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-1.7300</td>\n",
       "      <td>556499.82</td>\n",
       "      <td>1.293477e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20100106</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.72</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.30</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-1.7200</td>\n",
       "      <td>412143.13</td>\n",
       "      <td>9.444537e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20100107</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.05</td>\n",
       "      <td>22.40</td>\n",
       "      <td>22.65</td>\n",
       "      <td>22.90</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-1.0900</td>\n",
       "      <td>355336.85</td>\n",
       "      <td>8.041663e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20100108</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>22.35</td>\n",
       "      <td>22.60</td>\n",
       "      <td>22.65</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.2200</td>\n",
       "      <td>288543.06</td>\n",
       "      <td>6.506674e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2677</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20210423</td>\n",
       "      <td>23.32</td>\n",
       "      <td>23.65</td>\n",
       "      <td>23.07</td>\n",
       "      <td>23.29</td>\n",
       "      <td>22.98</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.3490</td>\n",
       "      <td>823230.15</td>\n",
       "      <td>1.920414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2678</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20210426</td>\n",
       "      <td>23.87</td>\n",
       "      <td>24.23</td>\n",
       "      <td>22.90</td>\n",
       "      <td>22.94</td>\n",
       "      <td>23.29</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-1.5028</td>\n",
       "      <td>872417.67</td>\n",
       "      <td>2.037110e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20210427</td>\n",
       "      <td>22.95</td>\n",
       "      <td>23.19</td>\n",
       "      <td>22.86</td>\n",
       "      <td>22.94</td>\n",
       "      <td>22.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>470431.40</td>\n",
       "      <td>1.083044e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2680</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20210428</td>\n",
       "      <td>23.29</td>\n",
       "      <td>23.45</td>\n",
       "      <td>22.78</td>\n",
       "      <td>23.35</td>\n",
       "      <td>22.94</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.7873</td>\n",
       "      <td>593837.93</td>\n",
       "      <td>1.375141e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20210429</td>\n",
       "      <td>23.34</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.11</td>\n",
       "      <td>23.59</td>\n",
       "      <td>23.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.0278</td>\n",
       "      <td>614836.88</td>\n",
       "      <td>1.439824e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2682 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0     000001.SZ   20100104  24.52  24.58  23.68  23.71      24.37   -0.66   \n",
       "1     000001.SZ   20100105  23.75  23.90  22.75  23.30      23.71   -0.41   \n",
       "2     000001.SZ   20100106  23.25  23.25  22.72  22.90      23.30   -0.40   \n",
       "3     000001.SZ   20100107  22.90  23.05  22.40  22.65      22.90   -0.25   \n",
       "4     000001.SZ   20100108  22.50  22.75  22.35  22.60      22.65   -0.05   \n",
       "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "2677  000001.SZ   20210423  23.32  23.65  23.07  23.29      22.98    0.31   \n",
       "2678  000001.SZ   20210426  23.87  24.23  22.90  22.94      23.29   -0.35   \n",
       "2679  000001.SZ   20210427  22.95  23.19  22.86  22.94      22.94    0.00   \n",
       "2680  000001.SZ   20210428  23.29  23.45  22.78  23.35      22.94    0.41   \n",
       "2681  000001.SZ   20210429  23.34  23.71  23.11  23.59      23.35    0.24   \n",
       "\n",
       "      pct_chg        vol        amount  \n",
       "0     -2.7100  241922.76  5.802495e+05  \n",
       "1     -1.7300  556499.82  1.293477e+06  \n",
       "2     -1.7200  412143.13  9.444537e+05  \n",
       "3     -1.0900  355336.85  8.041663e+05  \n",
       "4     -0.2200  288543.06  6.506674e+05  \n",
       "...       ...        ...           ...  \n",
       "2677   1.3490  823230.15  1.920414e+06  \n",
       "2678  -1.5028  872417.67  2.037110e+06  \n",
       "2679   0.0000  470431.40  1.083044e+06  \n",
       "2680   1.7873  593837.93  1.375141e+06  \n",
       "2681   1.0278  614836.88  1.439824e+06  \n",
       "\n",
       "[2682 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数据\n",
    "import tushare as ts\n",
    "# 设置token\n",
    "ts.set_token('7424c7ac953aebbb54e59f2624b47d5333cec6e3744bc0556a5c7538')\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api()\n",
    "# 根据时间段获取日交易数据, 000001招商银行, 时间段为：2010年1月1日-2020年1月1日\n",
    "data = pro.daily(ts_code='000001.SZ', start_date='20100101', end_data='20200101')\n",
    "data.sort_values(by='trade_date', inplace=True)\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "# 输出数据\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列经检验不平稳,p值为:0.08572163250370107\n"
     ]
    }
   ],
   "source": [
    "# 一、数据预处理\n",
    "# 时间序列预处理：平稳性检验\n",
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "diff=0\n",
    "# print(data['close'])\n",
    "adf=ADF(data['close'])\n",
    "# print(adf)\n",
    "if adf[1]>0.05:\n",
    "    print(u'原始序列经检验不平稳,p值为:%s' %(adf[1]))\n",
    "else:\n",
    "    print(u'原始序列经检验平稳,p值为:%s' %(adf[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列为非白噪声序列,p值为:0.0\n"
     ]
    }
   ],
   "source": [
    "# 时间序列预处理：纯随机性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "[[lb],[p]]=acorr_ljungbox(data['close'], lags=1,return_df=False)\n",
    "if p<0.05:\n",
    "    print(u'原始序列为非白噪声序列,p值为:%s'%p)\n",
    "else:\n",
    "    print(u'原始序列为白噪声序列,p值为:%s' %p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      close\n",
      "0     23.71\n",
      "1     23.30\n",
      "2     22.90\n",
      "3     22.65\n",
      "4     22.60\n",
      "...     ...\n",
      "2677  23.29\n",
      "2678  22.94\n",
      "2679  22.94\n",
      "2680  23.35\n",
      "2681  23.59\n",
      "\n",
      "[2682 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# 二、数据可视化\n",
    "import matplotlib.pyplot as plt\n",
    "data = data[['close']] # open开盘价, close收盘价, high最高价, low最低价\n",
    "print(data)\n",
    "# data.plot(subplots=True, figsize=(20, 12))\n",
    "# plt.title('000001 stock attributes from 2010-01-01 to 2020-01-01')\n",
    "# # plt.savefig('stocks.png')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三、模型的识别与定阶\n",
    "import pandas as pd\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "def plotds(xt, nlag=30, fig_size=(12, 10)):\n",
    "    if not isinstance(xt, pd.Series):\n",
    "        xt = pd.Series(xt)\n",
    "    plt.figure(figsize=fig_size)\n",
    "    layout = (2, 2)\n",
    "    \n",
    "    # 设置画图布局\n",
    "    ax_xt = plt.subplot2grid(layout, (0, 0), colspan=2) #最上面是xt图\n",
    "    ax_acf = plt.subplot2grid(layout, (1, 0)) # 左下角是acf图\n",
    "    ax_pacf = plt.subplot2grid(layout, (1, 1)) # 右下角是pacf图\n",
    "    \n",
    "    # 画出原始序列图像、自相关函数图像、偏相关函数图像\n",
    "    xt.plot(ax=ax_xt)\n",
    "    ax_xt.set_title('time series')\n",
    "    plot_acf(xt, lags=nlag, ax=ax_acf)\n",
    "    plot_pacf(xt, lags=nlag, ax=ax_pacf)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plotds(data['close'].dropna(), nlag=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0, 2335.540579146762],\n",
       " [1, 1, 2336.1044997078216],\n",
       " [1, 2, 2338.0627775868825],\n",
       " [1, 3, 2339.707514765354],\n",
       " [1, 4, 2340.661436977658],\n",
       " [2, 0, 2336.1171033387645],\n",
       " [2, 1, 2338.0817447767495],\n",
       " [2, 2, 2334.338793626569],\n",
       " [2, 3, 2336.0399485939706],\n",
       " [2, 4, 2337.9654984157696]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由上述ACF和PACF图已知，自相关函数是拖尾的，偏相关函数也是拖尾的，所以要选择ARMA模型，并需要进一步确定模型阶数\n",
    "import statsmodels.tsa.api as smtsa\n",
    "# 导入warnings，忽略warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "data_df=data.copy()\n",
    "aicVal=[]\n",
    "for ari in range(1, 3):\n",
    "    for maj in range(0, 5):\n",
    "        try:\n",
    "            arma_obj = smtsa.ARMA(data_df.close.tolist(), order=(ari, maj)).fit(maxlag=30, method='mle', trend='nc')\n",
    "            aicVal.append([ari, maj, arma_obj.aic])\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "# 输出aicVal\n",
    "aicVal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>   <td>2682</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 2)</td>    <th>  Log Likelihood     </th> <td>-1162.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>mle</td>       <th>  S.D. of innovations</th>   <td>0.373</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 29 Apr 2021</td> <th>  AIC                </th> <td>2334.339</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>17:06:48</td>     <th>  BIC                </th> <td>2363.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>2345.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>    0.0546</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.y</th> <td>    0.9452</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.y</th> <td>    0.9247</td> <td>    0.013</td> <td>   69.170</td> <td> 0.000</td> <td>    0.898</td> <td>    0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.y</th> <td>   -0.0349</td> <td>    0.019</td> <td>   -1.833</td> <td> 0.067</td> <td>   -0.072</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.0001</td> <td>          +0.0000j</td> <td>           1.0001</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>          -1.0579</td> <td>          +0.0000j</td> <td>           1.0579</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -1.0406</td> <td>          +0.0000j</td> <td>           1.0406</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th> <td>          27.5007</td> <td>          +0.0000j</td> <td>          27.5007</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 2682\n",
       "Model:                     ARMA(2, 2)   Log Likelihood               -1162.169\n",
       "Method:                           mle   S.D. of innovations              0.373\n",
       "Date:                Thu, 29 Apr 2021   AIC                           2334.339\n",
       "Time:                        17:06:48   BIC                           2363.810\n",
       "Sample:                             0   HQIC                          2345.000\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1.y        0.0546        nan        nan        nan         nan         nan\n",
       "ar.L2.y        0.9452        nan        nan        nan         nan         nan\n",
       "ma.L1.y        0.9247      0.013     69.170      0.000       0.898       0.951\n",
       "ma.L2.y       -0.0349      0.019     -1.833      0.067      -0.072       0.002\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.0001           +0.0000j            1.0001            0.0000\n",
       "AR.2           -1.0579           +0.0000j            1.0579            0.5000\n",
       "MA.1           -1.0406           +0.0000j            1.0406            0.5000\n",
       "MA.2           27.5007           +0.0000j           27.5007            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型拟合结果展示\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# method=mle 使用最大似然参数估计方法\n",
    "# order=(2,2) 代表模型阶数\n",
    "# trend=nc 控制确定性趋势的参数\n",
    "arma_obj_fin = smtsa.ARMA(data_df.close.tolist(), order=(2,2)).fit(maxlag=30, method='mle', trend='nc', disp=False)\n",
    "\n",
    "arma_obj_fin.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 四、模型预测 预测值与真实值对比\n",
    "data_df['ARMA'] = arma_obj_fin.predict()  # 将拟合结果存储到data_df变量，列名命名为ARMA\n",
    "f, axarr = plt.subplots(1, sharex=True)\n",
    "f.set_size_inches(12, 8)\n",
    "\n",
    "data_df['close'].iloc[len(data_df)-100:].plot(color='b', linestyle='-', ax=axarr)  # 真实数据图像\n",
    "data_df['ARMA'].iloc[len(data_df)-100:].plot(color='r', linestyle='-', ax=axarr)  # 拟合数据图像 \n",
    "axarr.set_title('ARMA(2,2)')  # 设置标题\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('close price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = arma_obj_fin.plot_predict(len(data_df)-50, len(data_df)+10)\n",
    "#print(len(data_df))\n",
    "fig = arma_obj_fin.plot_predict(len(data_df)-50, len(data_df)+10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.5740859 , 23.58057265, 23.56791287, 23.57335302, 23.56168444,\n",
       "       23.56618949, 23.55540671, 23.55907627, 23.54908512, 23.55200822])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测结果数值展示\n",
    "#predict = arma_obj_fin.predict(start=1, end=len(data_df)+10)\n",
    "predict = arma_obj_fin.predict(len(data_df)-50, len(data_df)+10)\n",
    "predict[len(predict)-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
